The field of the disclosure relates generally to data analysis, and more specifically, to systems and methods for identifying patterns in data using deep belief networks.
In various applications, raw data from a plurality of sensors and/or sources is organized and analyzed to extract useful information and/or intelligence. Because the information may be distributed among multiple sensors and/or sources, the data may be organized in the form of a graph that includes nodes and edges. Structures of interest (i.e. patterns) indicative of certain occurrences and/or instances may be identified using the nodes and the edges.
Complex social and information networks that include the raw data may include missing and/or incomplete information. Accordingly, to extract useful information and/or intelligence from such networks (i.e., the graph), data analysts (i.e., human operators) attempt to characterize the networks, locate common patterns in the networks, and query the networks for specific sub-structures. However, because such networks may be relatively large, data analysts typically utilize machine assistance to efficiently process the data.
To process the data effectively, the machine assistant generally requires some level of training. This training may be performed by the data analyst, or the system may be able to train itself. In general, the less intelligent the machine assistant, the more training must be performed by the data analyst. Accordingly, at least some known machine assistants require extensive training from the data analyst. Further, at least some known data analysis systems require the data analyst to constantly monitor operation of the machine assistant. Moreover, at least some known data analysis systems require that the data analyst have relatively sophisticated programming and artificial intelligence experience to effectively operate and train the machine assistant.